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Articles by G. Saha
Total Records ( 2 ) for G. Saha
  S. Ari , K. Sensharma and G. Saha
  Auscultation, the technique of listening to heart sounds, remains a primary detection tool for diagnosing heart valve disorders. Other techniques, e.g. electrocardiography, ultrasound, etc, are accurate as well as informative; but expensive. With major advancement in the speed of computers, heart sound signals can be processed with ease by memory efficient digital signal processing and pattern recognition algorithms. This paper presents a digital signal processor (DSP) implementation of such a technique by using new threshold criteria. The proposed work can detect whether a heart sound recording belongs to a person suffering from valvular heart disease or not by giving ‘diseased’ or ‘not diseased’ decisions. The algorithm is tested for nine commonly occurring pathological problems and normal heart sound. The robustness of the algorithm is also checked against synthetically injected additive white Gaussian noise (AWGN) with different SNR levels. It is found to give an accuracy of 96.67% up to SNR values of 15 dB and 93.33% up to SNR values of 5 dB.
  S. Ari , P. Kumar and G. Saha
  The first step towards detection of valvular heart diseases from heart sound signal (phonocardiogram) is segmentation. A segmentation algorithm provides the location of the first and second heart sounds which in turn helps to locate and analyse the murmur. Established phonocardiogram based segmentation methods use an electrocardiographic (ECG) signal as a continuous auxiliary input in a complex instrumentation setup. This paper proposes an automatic segmentation method that does not require any such auxiliary signal. Compared to other approaches without auxiliary signal, this work extensively utilizes biomedical domain features for reduction of time and computational complexities and is more accurate. The performance of the algorithm is evaluated for nine commonly occurring pathological cases and normal heart sound for various sampling frequencies, recording environments and age group of subjects. The proposed algorithm yields an overall accuracy of 97.47% and is compared with two competing techniques. In addition, the robustness of the algorithm is shown against additive white Gaussian noise contamination at various SNR levels.
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